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Forests are the largest terrestrial ecosystem carbon pool and provide the most important nature-based climate mitigation pathway. Compared with belowground biomass (BGB) and soil carbon, aboveground biomass (AGB) is more sensitive to human disturbance and climate change. Therefore, accurate forest AGB mapping will help us better assess the mitigation potential of forests against climate change. Here, we developed six models to estimate national forest AGB using six machine learning algorithms based on 52,415 spaceborne Light Detection and Ranging (LiDAR) footprints and 22 environmental features for China in 2007. The results showed that the ensemble model generated by the stacking algorithm performed best with a determination coefficient (R2) of 0.76 and a root mean square error (RMSE) of 22.40 Mg/ha. The verifications at pixel level (R2 = 0.78, RMSE = 16.08 Mg/ha) and provincial level (R2 = 0.53, RMSE = 14.05 Mg/ha) indicated the accuracy of the estimated forest AGB map is satisfactory. The forest AGB density of China was estimated to be 53.16 ± 1.63 Mg/ha, with a total of 11.00 ± 0.34 Pg. Net primary productivity (NPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), average annual rainfall, and annual temperature anomaly are the five most important environmental factors for forest AGB estimation. The forest AGB map we produced is expected to reduce the uncertainty of forest carbon source and sink estimations.

Bisulfite (BS)-based advanced oxidation processes (AOPs) are attractive for pollutant degradation, but often depend on costly transition metals with leaching risks. Herein, we report a citric acid-modified red mud catalyst (RMAC) for efficient Congo Red (CR) removal. Citric acid acted Simultaneously as an acid activator and carbon template, enlarging the surface area from 31.10 to 116.40 m 2 g −1 (3.74-fold increase). Under optimal conditions (5 mM BS, pH = 5, 80 mg L −1 CR), RMAC3-800 achieved 98.8% CR removal with a pseudo-first-order rate constant of 0.1399 min −1 and retained > 80% efficiency after three reuse cycles. Radical scavenging and EPR analyses confirmed SO 4 •− (53.7%) and •OH (46.3%) as the dominant species, whereas XPS identified Fe 0 as the principal active site. GC-MS detected six intermediates, supporting the proposed oxidative cleavage and mineralization pathways of the degradation process. A preliminary bench-scale cost analysis estimated an operating cost of ~ 13.94 RMB m −3 (≈ 1.95 USD m −3 ), underscoring its economic feasibility. This study demonstrates a cost-effective, recyclable, and sustainable catalytic system for wastewater treatment and red mud valorization.

Background and objective Electroencephalography (EEG) and neuroimaging measurements have been highly encouraged to be applied in clinics of disorders of consciousness (DOC) to improve consciousness detection. We tested the relationships between neural complexity measured on EEG and residual consciousness levels in DOC patients. Methods Resting-state EEG was recorded from twenty-five patients with DOC. Lempel–Ziv complexity (LZC) and permutation Lempel–Ziv complexity (PLZC) were measured on the EEG, and their relationships were analyzed with the consciousness levels of the patients. Results PLZC and LZC values significantly distinguished patients with a minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS), and healthy controls. PLZC was significantly correlated with the Coma Recovery Scale-Revised (CRS-R) scores of DOC patients in the global brain, particularly in electrodes locating in the anterior and posterior brain regions. Patients with higher CRS-R scores showed higher PLZC values. The significant difference in PLZC values between MCS and VS/UWS was mainly located in the bilateral frontal and right hemisphere regions. Conclusion Neural complexity measured on EEG correlates with residual consciousness levels of DOC patients. PLZC showed higher sensitivity than LZC in the classification of consciousness levels.

Sleep deprivation impairs core cognitive functions, such as working memory. Previous studies focused on global working memory performance, and direct comparisons of how different cognitive enhancers mitigate sleep deprivation-induced deficits in object working memory remain limited, particularly regarding the underlying neurophysiological mechanisms. This study compared the counteractive effects of caffeine and modafinil after 36 h of total sleep deprivation and elucidated their stage-specific neural mechanisms during object working memory processing. A randomized, double-blind, crossover design was used with 14 healthy male participants. Participants completed a 2-back object working memory task at baseline and after 36 h of sleep deprivation under caffeine and modafinil conditions. Event-related potentials were recorded to assess key component changes, including P2, N2, P3, and LPC. Behaviorally, modafinil significantly improved accuracy after sleep deprivation, surpassing baseline performance, with an exploratory trend toward shorter reaction times. In contrast, caffeine maintained behavioral performance at baseline levels without significant change. Neurophysiologically, caffeine markedly increased P2 amplitude but significantly decreased LPC amplitude after sleep deprivation, reflecting enhanced early perceptual processing alongside a decline in late-stage cognitive maintenance resources. However, modafinil stabilized P2 and LPC responses, suggesting improved neural efficiency and sustained top-down cognitive control during stimulus evaluation. Although caffeine and modafinil mitigate sleep deprivation induced decline in object working memory, they operate through distinct neural mechanisms. Caffeine relies on generalized compensatory arousal mechanisms, whereas modafinil exerts a more efficient and targeted enhancement of executive control. These findings provide electrophysiological evidence supporting the differential application of cognitive enhancers under extreme sleep loss.

Objectives To analyze the factors affecting prognosis of patients with sporadic amyotrophic lateral sclerosis (ALS), to establish a nomogram predictive model. Methods A total of 236 patients with sporadic ALS hospitalized in the Department of Neurology of the First Medical Center, Chinese PLA General Hospital, from March 2011 to November 2021 were enrolled in the study. Basic information and clinical and laboratory data of patients were collected, including sex, age at onset, body mass index, disease duration, diagnostic grade, and serum levels of creatine kinase (CK), creatinine (Cr), uric acid (UA), and ferritin. Kaplan-Meier univariate and multivariate Cox proportional hazard regression models were used to analyze the prognostic factors, and a nomogram predictive model was established. Results Univariate analysis showed that ferritin, CK, Cr, age at onset, disease duration, and body mass index (BMI) were all correlated with prognosis of ALS. Multivariate analysis showed that ferritin, Cr, disease duration, age at onset, and BMI were the strongest predictors. ROC curve and correction curve analyses verified the accuracy of the nomogram prediction model. Conclusions Ferritin, Cr, disease duration, age at onset, and BMI are independent predictors of survival in patients with ALS. Based on these clinical and biological prognostic factors, we established a quantitative model for predicting survival probability, and may assist in the prognostic evaluation of ALS, pending further validation.

Sleep loss is reported to affect postural control. However, the relationship between increased postural sway and the circadian rhythm (CR) remains unclear. To assess performance in the postural control test in aged individuals with an abnormal CR. This cross-sectional observational study included two groups of participants: those at high risk of falling (HFR) and those at low risk of falling (LFR), which was determined by the clinical cut-off score for the sway path with open eyes. Each participant wore an ActiGraph device on their non-dominant hand for 5-7 days. A non-parametric analysis of CR variables, including interdaily stability (IS), intraday variability (IV), relative amplitude (RA), interdaily coefficient of variation (ICV), etc., was used to evaluate the postural stability with a posturographic platform during a 30-s static balance test under the eyes closed (EC) and eyes open (EO) condition. Individuals in the HFR group demonstrated significantly higher scores in the Downton fall risk index (DFRI), higher ICV, and lower IS and M10 activity counts than the LFR group. Linear regression analysis revealed that under the EO condition, there was no association between postural control and CR disruption; however, under the EC condition, L5 was positively associated with variables reflecting an increase in postural sway. Increased postural sway was found to be associated with CR disruption in aged adults under the EC condition.

China’s university dormitories have high population densities, which can result in a large number of casualties because of crowding and stampedes during emergency evacuations. It is therefore important to plan properly for evacuations by mitigating the effect of choke points that create backlogs ahead of time. Accurate computer representations of the structure of a building and behavior of the evacuees are two important factors to obtain accurate evacuation time. In this paper, Agent-Based Modeling (ABM) and Building Information Modeling (BIM) are, respectively, implemented using the Unity platform to simulate the evacuation process. As a case study, the layout of a student dormitory building at Shanghai Normal University Xuhui District, Shanghai, China, is utilized along with the A* algorithm in Unity to explore the impact of evacuation speed and delays in creating choke points. Compared with previous research, the innovation of this study lies in: (1) using Unity software to make simulation of the physical environment both realistic and easy to implement, demonstrating Unity can be a well-developed platform to implement ABM-BIM research that focuses on crowd evacuation. (2) Using these simulations to evaluate different degrees of congestion caused by varying evacuation speeds, thus providing information about possible issues relating to evacuation efforts. Using the results, several recommended measures can be generated to help improve evacuation efficiency.

Background To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively. Methods We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature. Conclusion The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.

Actigraphy is widely used to detect a decline in physical activity in aged individuals with cerebral small vessel disease (cSVD). Disturbed fractal physical activity has been reported in aged adults with Alzheimer's disease (AD), mild cognitive impairment (MCI), and other neuropsychiatric disorders. To analyze the fractal physical activity pattern in elderly patients with cSVD. From May 2021 to August 2023, 55 patients with cSVD aged 60-80 years admitted to the seventh medical center of PLA General Hospital were included. The presence of lacunes, white matter hyperintensities, cerebral microbleeds, and perivascular spaces on magnetic resonance images (MRI) were rated independently. Furthermore, these MRI markers were summed in a score of 0-4, representing all cSVD features combined. Detrended fluctuation analysis (DFA) was used to evaluate the fractal physical activity fluctuations at multiple time scales. The relationship between the fractal physical activity pattern and physical activity and sleep quality was analyzed with partial Pearson correlation analysis. Individuals with a low severity cSVD burden showed a significant tendency toward a random fractal pattern relative to those with a higher severity cSVD burden. Similar results were obtained when comparing the lacune positive and negative groups. In aged adults with cSVD, fractal disturbance was associated with an average level of physical activity and not sleep quality. These findings demonstrate the presence of obvious fractal physical activity complexity in aged adults with cSVD.

Guaranteeing efficient evacuations in urban communities is critical for preserving lives, minimizing disaster impacts, and promoting community resilience. Challenges such as high population density, limited evacuation routes, and communication breakdowns complicate evacuation efforts. Vulnerable populations, urban infrastructure constraints, and the increasing frequency of disasters further contribute to the complexity. Despite these challenges, the importance of timely evacuations lies in safeguarding human safety, enabling rapid disaster response, preserving critical infrastructure, and reducing economic losses. Overcoming these hurdles necessitates comprehensive planning, investment in resilient infrastructure, effective communication strategies, and continuous community engagement to foster preparedness and enhance evacuation efficiency. This research looks into the complexities of evacuation dynamics within urban residential areas, placing a particular focus on the interaction between joint-rental arrangements and family ties and their influence on evacuation strategies during emergency situations. Using agent-based modeling, evacuation simulation scenarios are implemented using the Changhongfang community (Shanghai) while systematically exploring how diverse interpersonal relationships impact the efficiency of evacuation processes. The adopted methodology encompasses a series of group experiments designed to determine the optimal proportions of joint-rental occupants within the community. Furthermore, the research examines the impact of various exit selection strategies on evacuation efficiency. Simulation outcomes shed light on the fundamental role of interpersonal factors in shaping the outcomes of emergency evacuations. Additionally, this study emphasizes the critical importance of strategic exit selections, revealing their potential to significantly enhance overall evacuation efficiency in urban settings.